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首页> 外文期刊>Advances in space research >Automated crater detection algorithms from a machine learning perspective in the convolutional neural network era
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Automated crater detection algorithms from a machine learning perspective in the convolutional neural network era

机译：卷积神经网络时代从机器学习角度看的自动火山口检测算法

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Convolutional Neural Networks (CNN) offer promising opportunities to automatically glean scientifically relevant information directly from annotated images, without needing to handcraft features for detection. Crater counting started with hand counting hundreds, thousands, or even millions of craters in order to determine the age of geological units on planetary bodies of the solar system. Automated crater detection algorithms have attempted to speed up this process. Previous research has employed computer vision techniques with handcrafted features such as light and shadow patterns, circle finding, or edge detection. This research continues, but now some researchers use techniques like convolutional neural networks that enable the algorithm to develop its own features. As the field of machine learning undergoes exponential growth in terms of paper count and research methods, the crater counting application can benefit from the new research, especially when conducting joint interdisciplinary projects. Despite these advancements, the crater counting community has not yet adopted standard methods for automating the process despite decades of research. This survey enumerates challenges for both planetary geologists and machine learning researchers, looks at the recent automatic crater detection advancements using machine learning techniques (primarily in methods using CNNs), and makes recommendations for the path toward greater automation. (C) 2019 COSPAR. Published by Elsevier Ltd. All rights reserved.
机译：卷积神经网络（CNN）提供了有希望的机会，可以直接从带注释的图像中自动收集与科学相关的信息，而无需手工进行检测。火山口计数始于用手计数数百，数千甚至数百万个火山口，以确定太阳系行星体上地质单位的年龄。自动化的陨石坑检测算法已尝试加快这一过程。先前的研究采用了具有视觉特征的计算机视觉技术，例如光影图案，圆弧查找或边缘检测。这项研究仍在继续，但是现在一些研究人员使用卷积神经网络之类的技术，使该算法能够开发自己的功能。随着机器学习领域的论文数量和研究方法呈指数增长，火山口计数应用程序可以从新的研究中受益，特别是在开展联合跨学科项目时。尽管取得了这些进步，但尽管有数十年的研究，但火山口计数社区尚未采用使过程自动化的标准方法。这项调查列举了行星地质学家和机器学习研究人员所面临的挑战，研究了使用机器学习技术（主要是使用CNN的方法）在自动坑坑探测方面的最新进展，并提出了实现更高自动化程度的建议。 （C）2019 COSPAR。由Elsevier Ltd.出版。保留所有权利。

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